Molecular pathology is the examination at a molecular level of the DNA, mRNA, and proteins that cause or are otherwise associated with disease. Gene amplification and/or overexpression have been identified as an indicator of patient prognosis in a variety of tumors or for determining those patients that should be provided certain treatments. For example, a certain type of breast cancer is associated with an over-abundance (e.g., over expression) of the human epidermal growth factor 2 (“HER2”) versus the number of chromosome 17s found in the cell. Sadly, this alteration is also an independent prognostic factor predictive of poor clinical outcome and a high risk of recurrence. By detecting the number of HER2 genes versus the number of chromosome 17s in a tissue sample, this particular type of breast cancer can be more readily identified and treatment options can be evaluated.
In-situ hybridization can be used to look for the presence of a genetic abnormality or condition such as amplification of cancer causing genes specifically in cells that, when viewed under a microscope, morphologically appear to be malignant. In situ hybridization (ISH) employs labeled DNA or RNA probe molecules that are anti-sense to a target gene sequence or transcript to detect or localize targeted nucleic acid target genes within a cell or tissue sample. ISH is performed by exposing a cell or tissue sample immobilized on a glass slide to a labeled nucleic acid probe which is capable of specifically hybridizing to a given target gene in the cell or tissue sample. Several target genes can be simultaneously analyzed by exposing a cell or tissue sample to a plurality of nucleic acid probes that have been labeled with a plurality of different nucleic acid tags. By utilizing labels having different emission wavelengths, simultaneous multicolored analysis may be performed in a single step on a single target cell or tissue sample. For example, INFORM HER2 Dual ISH DNA Probe Cocktail Assay from Ventana Medical Systems, Inc., is intended to determine HER2 gene status by enumeration of the ratio of the HER2 gene to Chromosome 17. The HER2 and Chromosome 17 probes are detected using a two color chromogenic ISH in formalin-fixed, paraffin-embedded human breast cancer tissue specimens.
Digital microscopy systems have been introduced wherein tissue samples are prepared in the usual way of being mounted on glass slides, but instead of having the pathologist view the samples using a manually controlled optical microscope, the slides are processed using digital imaging equipment. In recent years, digital pathology has transformed from the use of camera-equipped microscopes to high-throughput digital scanning of whole tissue samples. This development not only enables virtual storing and sharing of biological data, but it also improves the turnaround times for the pathologist and the patient.
The digitization of biological data has enabled the use of computers assisting in the diagnosis. The dramatic increase of computer power over the past decades, together with the development of advanced image analysis algorithms, has allowed the development of computer-assisted approaches capable of analyzing the bio-medical data. Interpreting tissue slides manually is labor intensive, costly and involves the risk of human errors and inconsistency, while using automated image analysis can provide additional automatic, fast and reproducible analyses, assisting the pathologist in making an accurate and timely diagnosis.
The importance of nucleus-vs-background segmentation is explained in the context of the primary aim of digital pathology—to analyze and score the stained tissue slides. In some examples, when the cell nucleus is stained indicating positive/negative tumor staining, then the nuclei need to be detected and then classified into the right category—e.g. positive tumor nuclei, negative tumor nuclei, nuclei of stromal cells, of lymphocytes, and so on. For the correct classification, it is often required that certain features corresponding to nucleus shape, size, need to be computed and hence, accurate nucleus-vs-background segmentation, in the following referred to as nucleus segmentation, is required. In the context of Dual ISH images, nuclei which are particularly suited for scoring by a downstream image analysis system or a pathologist need to be identified. Thus, for Dual ISH images, the requirement from a nucleus segmentation method is that only those nuclei should be picked, where the dots can be clearly detected and where the dot colors can be clearly identified. Also, if the nucleus is not isolated, then incorrect segmentation can result in a nucleus boundary extending to a nearby nucleus, resulting in nearby dots also being counted giving an erroneous count. The opposite scenario of a proper nucleus boundary being only partially identified may also lead to genuine dots being missed. Ultimately, incorrect segmentation can produce an incorrect black-to-red score.
In general, the process of segmentation aims to separate the cell nuclei from the background (e.g. cytoplasm) in any histological image or series of images. Segmentation of cell nuclei is believed to be a challenging problem due to wide size and shape variations of the nuclei, complex heterogeneity within nucleus and in stroma, improper staining, imaging artifacts, variation in density of the nuclei in an image and across different images, and occluded nuclei.
There remains a need for an improved method of nucleus detection and segmentation that provides a high level of quality and accuracy.